👀 Views: 41 💬 Answers: 1 📅 Created: 2025-06-16

r dplyr tibble R

I'm integrating two systems and I'm encountering an issue when trying to create a new column in a `tibble` using `mutate()` along with `if_else()`. Specifically, I'm trying to categorize a numeric column into bins, but I'm getting unexpected results. Here's the code I'm using: ```R library(dplyr) # Sample tibble my_data <- tibble( value = c(1, 2, 3, 4, 5, NA) ) # Attempting to create a new column 'category' my_data <- my_data %>% mutate(category = if_else(value < 3, 'low', 'high')) ``` After running this code, I expect the `category` column to contain 'low' for values 1 and 2, and 'high' for 3, 4, and 5. However, I'm seeing that the `category` column is returning `NA` for the row with `NA` in the `value` column. I thought `if_else()` would handle `NA` values by returning `NA` in the resulting column, but it seems to be propagating the `NA` more unexpectedly. I've checked my `dplyr` version, which is 1.0.7, and it seems to be up to date. I also tried using `mutate()` with a simple `ifelse()` function instead, but I face the same issue. Here's the alternative I attempted: ```R my_data <- my_data %>% mutate(category = ifelse(is.na(value), NA, ifelse(value < 3, 'low', 'high'))) ``` This also results in the same unexpected behavior. Could it be that I am misunderstanding how `if_else()` processes `NA` values? Is there a better way to handle this situation while ensuring that I get clear categories for my data without unwanted `NA` values in the `category` column? Any insights would be greatly appreciated. The project is a REST API built with R.